Context, Memory & RAG tools for LangChain / LangGraph

LangChain's CLI that writes and maintains agent-readable wikis for codebases or purpose memory, turning documentation drift into a versioned, automatable harness artifact. Emits Google Open Knowledge Format bundles so curated context stays portable across agents and can be kept fresh via CI

Stream large multimodal datasets to achieve near 100% GPU utilization. Query, visualize, & version control data. Access data w/o the need to recompute the embeddings for the model finetuning

Easiest and laziest way for building multi-agent LLMs applications.

The AI-Native Search Database. Best for agent storage, it unifies vector, text, structured, and semi-structured data into a single engine. This all-in-one database makes agents smarter, easier to run, and more stable.

Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.

Agent Systems Handbook by Prompthon — A practical AI agents handbook covering agent systems, agentic workflows, LangGraph, MCP/A2A, context engineering, agent memory, evaluation, observability, and multi-agent architecture. Current trend focus: Gemini Interactions API and managed agents, emerging agent runtimes, and production AI workflow patterns.

A streamlit app to dialog with your second brain notes using OpenAI and ChromaDB locally. github